Zhao et al. (2025) A Spatial-Temporal Seamless Evapotranspiration Product Based on TSEB-SM Model and DNN-ETo Method Across China

Identification

• Journal: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• Year: 2025
• Date: 2025-12-11
• Authors: Gengle Zhao, Lisheng Song, William P. Kustas, Long Zhao, Shaomin Liu, Gaofei Yin, Rongqi Tang
• DOI: 10.1109/jstars.2025.3642952

Research Groups

Not available in the provided text.

Short Summary

This paper aims to develop a novel spatial-temporal seamless evapotranspiration product for China by integrating the TSEB-SM model with a Deep Neural Network (DNN) approach for reference evapotranspiration.

Objective

• To develop and validate a spatial-temporal seamless evapotranspiration product across China by combining the Two-Source Energy Balance (TSEB) model with a soil moisture (SM) component and a Deep Neural Network (DNN) method for estimating reference evapotranspiration (ETo).

Study Configuration

• Spatial Scale: China (country-level)
• Temporal Scale: Seamless (implying continuous, likely daily or sub-daily, but specific resolution not given)

Methodology and Data

• Models used: TSEB-SM Model, DNN-ETo Method
• Data sources: Not available in the provided text.

Main Results

Not available in the provided text.

Contributions

Not available in the provided text.

Funding

Not available in the provided text.

Citation

@article{Zhao2025SpatialTemporal,
  author = {Zhao, Gengle and Song, Lisheng and Kustas, William P. and Zhao, Long and Liu, Shaomin and Yin, Gaofei and Tang, Rongqi},
  title = {A Spatial-Temporal Seamless Evapotranspiration Product Based on TSEB-SM Model and DNN-ETo Method Across China},
  journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  year = {2025},
  doi = {10.1109/jstars.2025.3642952},
  url = {https://doi.org/10.1109/jstars.2025.3642952}
}